# Simultaneous Edit-Imputation for Continuous Microdata Package

### Description

The package implements an integrated editing and imputation for continuous microdata under linear constraints. It relies on a Bayesian nonparametric hierarchical modeling approach in which the joint distribution of the data is estimated by a flexible joint probability model. The generated edit-imputed data are guaranteed to satisfy all imposed edit rules, whose types include ratio edits, balance edits and range restrictions.

### Details

Package: | EditImputeCont |

Type: | Package |

License: | GPL (>= 3) |

### Author(s)

Quanli Wang, Hang J. Kim, Jerome P. Reiter, Lawrence H. Cox and Alan F. Karr

Maintainer: Quanli Wang <quanli@stat.duke.edu> and Hang J. Kim <hangkim0@gmail.com>

### References

Hang J. Kim, Lawrence H. Cox, Alan F. Karr, Jerome P. Reiter and Quanli Wang (2015). "Simultaneous Edit-Imputation for Continuous Microdata", Journal of the American Statistical Association, DOI: 10.1080/01621459.2015.1040881.

### See Also

`readData`

, `createModel`

, `multipleEI`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
library(EditImputeCont)
## read the toy example data, which has two ratio edits and a balance edit
data(SimpleEx)
data1 = readData(Y.original=SimpleEx$D.obs, ratio=SimpleEx$Ratio.edit,
range=NULL, balance=SimpleEx$Balance.edit)
## create and initialize the model with 15 DP mixture components
model1 = createModel(data.obj=data1, K=15)
## Run an iteration of MCMC
# model1$Iterate()
# dim(model1$Y.edited)
## [1] 1000 4 # Edit-imputed datasets of n=1000 records with p=4 variables
## Please see the example in the demo folder for more detailed explanation
``` |